We have introduced a number of curvature measures and derived flows, as well as developing a family of coarse embedding kernels derived from some of them or from related operators. Furthermore, we have illustrated the ideas above on a sample of small and medium size networks, as well as on test images. However, these can represent only a preamble to an in-depth
examination of these new tools. Therefore, first and foremost, there is the need for systematic experiments with large scale networks. It should be noted that his construction of networks, from manifolds to triangulations, and their subsequent simplification via curvature-based sampling is of high relevance in Deep Learning [1]}, [2]}.
